Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling
Zichang Tan1,2; Shuai Zhou1,3; Jun Wan1,2; Zhen Lei1,2; Stan Z. Li1,2
2016
会议名称13th Asian Conference on Computer Vision
会议日期November 20-24, 2016
会议地点Taipei, Taiwan
摘要In this paper, we propose a novel approach based on a single convolutional neural network (CNN) for age estimation. In our proposed network architecture, we first model the randomness of aging with the Gaussian distribution which is used to calculate the Gaussian integral of an age interval. Then, we present a soft softmax regression function used in the network. The new function applies the aging modeling to compute the function loss. Compared with the traditional softmax function, the new function considers not only the chronological age but also the interval nearby true age. Moreover, owing to the complex of Gaussian integral in soft softmax function, a look up table is built to accelerate this process. All the integrals of age values are calculated offline in advance. We evaluate our method on two public datasets: MORPH II and Cross-Age Celebrity Dataset (CACD), and experimental results have shown that the proposed method has gained superior performances compared to the state of the art.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/15298
专题模式识别国家重点实验室_生物识别与安全技术研究
通讯作者Jun Wan
作者单位1.Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences
3.Faculty of Information Technology, Macau University of Science and Technology, Macau
推荐引用方式
GB/T 7714
Zichang Tan,Shuai Zhou,Jun Wan,et al. Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling[C],2016.
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